ML Platform Engineer: Scalable Research Pipelines

Responsibilities

• Lead the design and development of a scalable, reliable, and reproducible machine learning research platform.
• Build infrastructure to support large-scale experimentation, model training, and simulation across both on‑premise high‑performance compute environments and multi‑cloud setups.
• Work closely with researchers to understand evolving workflows and translate those needs into robust platform capabilities.
• Architect and optimize distributed training pipelines for high-throughput, GPU‑accelerated workloads.
• Enhance experiment management, model versioning, artifact tracking, and data lineage to ensure transparent and repeatable research processes.
• Develop tools and frameworks that improve feature engineering, dataset creation, and large-scale backtesting.
• Drive initiatives to improve compute efficiency, resource allocation, and workload isolation across heterogeneous environments.
• Enhance platform observability with improved metrics, logging, tracing, and debugging capabilities tailored to ML and distributed systems.
• Support rapid iteration by delivering features and fixes quickly while maintaining strong engineering standards.
• Contribute to long-term architectural planning to ensure the platform scales with growing data volumes and model complexity.

Qualifications

• 2+ years of experience designing and building distributed systems at scale, ideally supporting research or data-heavy workloads.
• Strong programming skills in Python with a focus on clean, maintainable, high-performance code.
• Experience running applications on Linux-based HPC clusters and/or cloud computing platforms.
• Solid understanding of distributed computing, parallel processing, and resource management.
• Hands-on experience with GPU workloads and familiarity with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience optimizing data pipelines and handling large structured and unstructured datasets.
• Strong debugging skills with the ability to diagnose issues across multiple layers of the stack.
• Comfortable working independently in a fast-paced, research-oriented environment.
• Strong communication skills and experience collaborating directly with researchers or data-focused teams.

Preferred Attributes

• Experience building internal ML platforms or research tooling at scale.
• Familiarity with experiment‑tracking tools, workflow orchestration systems, and model lifecycle management.
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Exposure to high-performance or latency-sensitive domains such as quantitative research, simulation systems, or large‑scale distributed compute.